Identifying Bias in Machine Learning: Fair Models with TensorFlow

Machine learning models are only as good as the data used to train them. If your training datasets contain hidden biases, your neural networks will make unfair, inaccurate, or skewed predictions. This course provides a clear, step-by-step foundation in recognizing and correcting bias in computer vision and image classification models. Through clear explanations and written code walkthroughs, you will discover how neural networks process visual information and how to audit your data for fairness. You will learn to pinpoint where a model is focusing its attention and how to adjust your training pipeline to ensure balanced, reliable outcomes. What you'll learn: - Understand the core concepts of algorithmic bias and how it enters machine learning pipelines - Analyze neural network decision-making to see where a model focuses its attention on an image - Implement data preprocessing and augmentation techniques in TensorFlow to balance skewed datasets - Audit training data to identify underrepresented classes and systematic errors - Build and train fairer image classification models using Keras - Apply modern validation strategies to test models for equitable performance across different groups This course begins with essential definitions of machine learning ethics and dataset preparation, then guides you through practical code implementations for inspecting and refining your models. This text-only course is designed for beginners, developers, and aspiring data scientists who want to build ethical AI. No prior machine learning experience is required to get started. Start reading today to master the fundamentals of building fair and reliable machine learning models.